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Methods for Combining Statistical Models of Music

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Computer Music Modeling and Retrieval (CMMR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3310))

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Abstract

The paper concerns the use of multiple viewpoint representation schemes for prediction with statistical models of monophonic music. We present an experimental comparison of the performance of two techniques for combining predictions within the multiple viewpoint framework. The results demonstrate that a new technique based on a weighted geometric mean outperforms existing techniques. This finding is discussed in terms of previous research in machine learning.

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Author information

Authors and Affiliations

  1. Centre for Computational Creativity, City University, Northampton Square, London, EC1V 0HB, UK

    Marcus Pearce, Darrell Conklin & Geraint Wiggins

Authors
  1. Marcus Pearce
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  2. Darrell Conklin
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  3. Geraint Wiggins
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Editor information

Editors and Affiliations

  1. Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campus 55, 5230, Odense M, Denmark

    Uffe Kock Wiil

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Pearce, M., Conklin, D., Wiggins, G. (2005). Methods for Combining Statistical Models of Music. In: Wiil, U.K. (eds) Computer Music Modeling and Retrieval. CMMR 2004. Lecture Notes in Computer Science, vol 3310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31807-1_22

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  • DOI: https://doi.org/10.1007/978-3-540-31807-1_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24458-5

  • Online ISBN: 978-3-540-31807-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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